Abstract

A basic building block in many high-level Computer Vision tasks such as image classification, object detection, image retrieval, image segmentation, etc is the notion of a distance or similarity between images and/or parts thereof. This is conveniently formalized in the concept of distance functions and kernels, that can be used with many existing algorithms such as large margin classifiers or nearest neighbour algorithms. The importance of suitable distances and kernels is reflected in the large number of publications at major computer vision conferences. Researchers have mainly pursued two different routes, a) either encoding human prior knowledge in manual guided design, or b) learning based approaches that try to infer these functions automatically from training data. In particular, the latter have been applied quite successfully to the aforementioned tasks as evident from the ever increasing performance results on the standard benchmark datasets. However diverse problems persists and many approaches do employ only simple distance and kernel functions that are not tailored to the Computer Vision problem specifics.

Workshop Format

This workshop is designed to target both experts as well as novices
in both the field of kernel and distance learning. To meet
this purpose we include an overview presentation into the
fields. This way the non-expert audience will get an
overview of current state-of-the-art techniques and
developments on both topics. The invited speakers will
give a more detailed presentation of their own work and
discuss potential next goals.

The workshop will include the following contributions:

Overview/Introduction to Kernel Learning

Overview/Introduction to Distance Learning

Presentation of selected publications by invited speakers

Contributions as posters and talks about ongoing and finished work

Call For Contribution

We invite authors to submit abstracts of relevant research for
presentation at the workshop. Topics relevant to the
workshop include (but are not limited to) ongoing
research efforts related to distances and kernels in
computer vision, such as novel algorithmic formulations,
applications of distance/kernel learning in vision, or
empirical evaluations of existing techniques.

Abstracts should be submitted as .pdf or .txt files, and should not
exceed two pages. Each submission will be reviewed by
members of the workshop committee, and successful
abstracts will be selected for presentation at the
workshop as either oral or poster
presentations. Submission deadline is 28.August 2011 (see
dates)

Please note that we do not plan workshop proceedings. The intention
of the workshop is to provide an overview over recent
advances which is why we also invite to present
already published work. Since there are no
proceedings you can also submit work in progress that has not
been published so far without spoiling the chance of
submitting in the future.